Lately, there is a growing interest in conducting research on optimization algorithms due to their wide range of engineering applications. One of the optimization algorithms' categories is evolutionary algorithms which are inspired from the natural behavior of animals and humans. Further, each of the evolutionary algorithms has its own advantages and disadvantages in convergence accuracy and computational time. In the present paper, a novel solution search algorithm taken from the team games is introduced. This evolutionary algorithm named Team Game Algorithm (TGA) involves passing a ball, making mistakes and substitution operators. Comparing the TGA's results to the outcomes of other well-known algorithms for unimodal and multimodal test functions elucidates the successful design of the proposed heuristic algorithm.
"TGA: Team game algorithm,"
Future Computing and Informatics Journal: Vol. 3:
2, Article 6.
Available at: https://digitalcommons.aaru.edu.jo/fcij/vol3/iss2/6